1 00:00:15,356 --> 00:00:21,876 Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the show 2 00:00:21,916 --> 00:00:24,836 Speaker 1: where we explore the stories behind the stories in the news. 3 00:00:25,356 --> 00:00:29,956 Speaker 1: I'm Noah Feldment. Ever since COVID nineteen broke out, I 4 00:00:29,996 --> 00:00:34,396 Speaker 1: have been extremely eager to hear the thoughts of Partis Zabetti. 5 00:00:35,156 --> 00:00:38,236 Speaker 1: Partis has actually been on the show before this outbreak, 6 00:00:38,636 --> 00:00:43,396 Speaker 1: talking about her extraordinary work on ebola. She's a computational 7 00:00:43,436 --> 00:00:46,916 Speaker 1: biologist in the Harvard Biology Department in the Broad Institute. 8 00:00:47,236 --> 00:00:49,796 Speaker 1: She's also a professor in the Department of Immunology and 9 00:00:49,836 --> 00:00:52,716 Speaker 1: Infectious Disease at the Harvard chance School of Public Health. 10 00:00:53,156 --> 00:00:55,996 Speaker 1: In short, Pardis is the person who knows the most 11 00:00:56,116 --> 00:01:00,076 Speaker 1: of anybody that I know about the complexity of developing 12 00:01:00,156 --> 00:01:04,156 Speaker 1: understanding of a virus and what you do under circumstances 13 00:01:04,196 --> 00:01:08,676 Speaker 1: of outbreak. I knew that whatever Partis was thinking about 14 00:01:08,836 --> 00:01:12,116 Speaker 1: right in this moment would be fascinating and significant and 15 00:01:12,196 --> 00:01:15,716 Speaker 1: would shed new light on the circumstances that we're facing, 16 00:01:16,036 --> 00:01:19,276 Speaker 1: how we reopen, how we track the virus, and how 17 00:01:19,316 --> 00:01:23,876 Speaker 1: we reach a vaccine. And sure enough, Parties said extraordinary 18 00:01:23,916 --> 00:01:28,276 Speaker 1: things to say on all of the above. Partis, thank 19 00:01:28,316 --> 00:01:31,476 Speaker 1: you so much for being with me when we last spoke, 20 00:01:31,556 --> 00:01:33,316 Speaker 1: which is just a few months ago, but it was 21 00:01:33,396 --> 00:01:38,156 Speaker 1: before the coronavirus really hit. We talked extensively about your 22 00:01:38,796 --> 00:01:43,116 Speaker 1: experience in addressing pandemics in Africa, and in just a moment, 23 00:01:43,116 --> 00:01:46,116 Speaker 1: I want to ask you about Africa and coronavirus, but 24 00:01:46,196 --> 00:01:48,076 Speaker 1: first I want to ask you about what it means 25 00:01:48,076 --> 00:01:50,436 Speaker 1: for you that suddenly the work that you've been doing 26 00:01:50,516 --> 00:01:53,796 Speaker 1: for years is literally in your front yard right here 27 00:01:53,796 --> 00:01:57,956 Speaker 1: in Massachusetts. I've actually been working with the Massachusetts Department 28 00:01:57,956 --> 00:02:02,196 Speaker 1: of Health for many years, and in fact actually do 29 00:02:02,276 --> 00:02:04,396 Speaker 1: as much work in my own backyard. We've been seeing 30 00:02:04,396 --> 00:02:07,236 Speaker 1: outbreaks in our own backyards for many, many years, and 31 00:02:07,716 --> 00:02:10,876 Speaker 1: I became really passionate about as part of the Harvard 32 00:02:11,316 --> 00:02:15,356 Speaker 1: Outbreak Response Committee, started seeing cases of momps in the 33 00:02:15,396 --> 00:02:19,676 Speaker 1: sports teams at Harvard University that then moved into the 34 00:02:19,916 --> 00:02:23,196 Speaker 1: residential homes, and then to the freshman dining hall where 35 00:02:23,236 --> 00:02:27,076 Speaker 1: staff were infected, and to health services where nurses were infected. 36 00:02:27,116 --> 00:02:30,036 Speaker 1: And so I have long been passionate about the spread 37 00:02:30,036 --> 00:02:33,796 Speaker 1: of infectious diseases here in Massachusetts and in residential colleges 38 00:02:33,836 --> 00:02:36,796 Speaker 1: more generally, knowing that they're one of the places where 39 00:02:36,796 --> 00:02:40,396 Speaker 1: you have the most high risk of infection spreading because 40 00:02:40,396 --> 00:02:42,476 Speaker 1: you have lots of students from around the country who 41 00:02:42,516 --> 00:02:44,676 Speaker 1: don't know each other now living with each other on 42 00:02:44,716 --> 00:02:47,156 Speaker 1: top of each other. So this is something we have 43 00:02:47,276 --> 00:02:49,436 Speaker 1: been concerned about and thinking about for a long time. 44 00:02:49,636 --> 00:02:52,196 Speaker 1: So none of this is news to you, but at 45 00:02:52,236 --> 00:02:54,556 Speaker 1: least in the United States, many people are really for 46 00:02:54,596 --> 00:02:57,916 Speaker 1: the first time waking up to the realities of pandemic 47 00:02:57,956 --> 00:03:01,276 Speaker 1: effects for them immediately at home. Does that strike you 48 00:03:01,356 --> 00:03:05,996 Speaker 1: as an opportunity to change people's perspectives on something that 49 00:03:06,116 --> 00:03:09,876 Speaker 1: has been one of your central research vocide for many years. Um, 50 00:03:10,236 --> 00:03:13,356 Speaker 1: you know it is, But when the way I think 51 00:03:13,356 --> 00:03:15,916 Speaker 1: about things, uh, it's not. That's like not where my 52 00:03:15,956 --> 00:03:18,276 Speaker 1: head is at. You know, my head is at what 53 00:03:18,396 --> 00:03:21,036 Speaker 1: is this lockdown doing to our communities? Seeing the rise 54 00:03:21,036 --> 00:03:24,796 Speaker 1: of suicide rates and you know, eight hundred percent, like 55 00:03:24,836 --> 00:03:29,156 Speaker 1: increase in suicide line usage and domestic violence being up 56 00:03:29,156 --> 00:03:31,876 Speaker 1: and mental health being up. I find myself just very 57 00:03:32,156 --> 00:03:34,516 Speaker 1: in the here and now of the existential threat that 58 00:03:34,516 --> 00:03:38,356 Speaker 1: we're facing to our entire way of life, and it 59 00:03:38,476 --> 00:03:42,796 Speaker 1: is frustrating to you know, have been one of many 60 00:03:42,836 --> 00:03:45,116 Speaker 1: people who have been calling for more attention to this 61 00:03:45,196 --> 00:03:48,756 Speaker 1: for many many years. Um, there's there's all of that 62 00:03:48,796 --> 00:03:51,236 Speaker 1: frustration from the past and all of that. Know that 63 00:03:51,276 --> 00:03:54,516 Speaker 1: there's an opportunity in the future to try to change this, 64 00:03:54,596 --> 00:03:57,036 Speaker 1: but right now we have to deal with this crisis, um, 65 00:03:57,076 --> 00:03:59,756 Speaker 1: and just I'm just spending all of my mental capacity 66 00:03:59,796 --> 00:04:02,396 Speaker 1: just trying to figure out how to best help navigate 67 00:04:02,436 --> 00:04:04,036 Speaker 1: and right now just even thinking about how do we 68 00:04:04,076 --> 00:04:06,596 Speaker 1: bring how do we or if we bring college kids 69 00:04:06,636 --> 00:04:08,876 Speaker 1: back to campus. I mean, there's just so many very 70 00:04:08,876 --> 00:04:12,036 Speaker 1: specific needs that we have right now. Is there any 71 00:04:12,036 --> 00:04:15,916 Speaker 1: way to bring college kids back to campus in September 72 00:04:15,996 --> 00:04:18,116 Speaker 1: anywhere in the United States right now? Is there a 73 00:04:18,116 --> 00:04:21,476 Speaker 1: credible path that you can see where that happens. Well, 74 00:04:22,796 --> 00:04:26,156 Speaker 1: that's what I'm thinking through. Before the outbreak, I had 75 00:04:26,196 --> 00:04:30,556 Speaker 1: actually written a number of proposals that I sent around 76 00:04:30,716 --> 00:04:33,596 Speaker 1: and actually couldn't get funded. But I actually had a 77 00:04:33,596 --> 00:04:36,036 Speaker 1: piece of it that was a mobile app that students 78 00:04:36,156 --> 00:04:38,916 Speaker 1: use at trace contacts and that help them help us 79 00:04:38,956 --> 00:04:42,396 Speaker 1: stop infections happening on college campuses and in a lot 80 00:04:42,436 --> 00:04:44,116 Speaker 1: of ways, like if you think about a college campuses, 81 00:04:44,156 --> 00:04:46,916 Speaker 1: their contacts are most known. You know, they're going to 82 00:04:46,956 --> 00:04:49,436 Speaker 1: be in the interno genetics class and back in their 83 00:04:49,436 --> 00:04:51,396 Speaker 1: dorm and on their sports team. There's like three places 84 00:04:51,396 --> 00:04:53,076 Speaker 1: they can be, and so it's actually a place where 85 00:04:53,676 --> 00:04:55,916 Speaker 1: I actually in the proposal I always had, I said 86 00:04:56,236 --> 00:05:00,396 Speaker 1: that college campuses are the best place to model outbreak response. 87 00:05:00,516 --> 00:05:03,076 Speaker 1: They're the most vulnerable, but they're also really a great 88 00:05:03,116 --> 00:05:05,076 Speaker 1: audience in which to try to build such a system. 89 00:05:05,516 --> 00:05:11,156 Speaker 1: The path would be extraordinary surveillance campuses. Like one of 90 00:05:11,156 --> 00:05:13,756 Speaker 1: my students went to Middlebury, and you know, Middlebury is 91 00:05:13,796 --> 00:05:17,996 Speaker 1: thirty four miles away from the nearest other city. She 92 00:05:18,076 --> 00:05:19,916 Speaker 1: sort of argues, why did they even send the students 93 00:05:19,956 --> 00:05:24,756 Speaker 1: home when there was no cases of a coronavirus at 94 00:05:24,796 --> 00:05:27,556 Speaker 1: Middlebury and only one in the state of Vermont. You 95 00:05:27,636 --> 00:05:30,116 Speaker 1: just put them at more risk. Or if they happen 96 00:05:30,156 --> 00:05:32,196 Speaker 1: to be infected unbeknownst to them, you just sent their 97 00:05:32,196 --> 00:05:34,476 Speaker 1: parents at risk. Right, There's there's a lot of questions 98 00:05:34,516 --> 00:05:37,836 Speaker 1: about that. I do think that if we had extraordinary 99 00:05:37,876 --> 00:05:40,836 Speaker 1: amounts of surveillance and real buy in from the students 100 00:05:40,876 --> 00:05:43,876 Speaker 1: who wanted to be there on how to manage and 101 00:05:43,916 --> 00:05:46,716 Speaker 1: test for COVID and protections for the staff who would 102 00:05:46,716 --> 00:05:48,916 Speaker 1: be engaged with them, you could bring them back. I 103 00:05:48,916 --> 00:05:51,116 Speaker 1: would imagine that it. The way that it would have 104 00:05:51,156 --> 00:05:54,636 Speaker 1: to happen is not by creating social distancing between individuals, 105 00:05:54,796 --> 00:05:58,396 Speaker 1: but creating a circuit around those people. Right. A closed loop, 106 00:05:58,476 --> 00:06:01,076 Speaker 1: in my mind, is the way we don't actually need 107 00:06:01,556 --> 00:06:03,756 Speaker 1: to all social distance from each other. We need to 108 00:06:04,036 --> 00:06:08,236 Speaker 1: have closed circuits and a lot of close monitoring amongst 109 00:06:08,236 --> 00:06:09,836 Speaker 1: those closed circuits. I think that would be the way. 110 00:06:09,836 --> 00:06:13,276 Speaker 1: It's almost like a tribal or communal version of it. 111 00:06:13,276 --> 00:06:16,916 Speaker 1: It's really fascinating, and it's counterintuitive because one thing that 112 00:06:16,916 --> 00:06:21,276 Speaker 1: I've heard from some university administrators is the dorms. The dorms, 113 00:06:21,276 --> 00:06:24,116 Speaker 1: we have the problem that we can't really have students 114 00:06:24,116 --> 00:06:27,396 Speaker 1: back to campus potentially because we might get rapids spread 115 00:06:27,556 --> 00:06:29,716 Speaker 1: of the virus in the dorms. It would be one 116 00:06:29,756 --> 00:06:31,116 Speaker 1: thing some of them have said, if we could have 117 00:06:31,156 --> 00:06:33,796 Speaker 1: social distancing and people lived off of campus, but if 118 00:06:33,796 --> 00:06:35,236 Speaker 1: they all live together, that makes it worse than what 119 00:06:35,276 --> 00:06:37,156 Speaker 1: you're saying is one hundred and eighty degrees the opposite. 120 00:06:37,396 --> 00:06:39,996 Speaker 1: You're saying that it's precisely the fact that you could 121 00:06:39,996 --> 00:06:44,556 Speaker 1: have a relatively self contained college community or university community, 122 00:06:45,076 --> 00:06:47,316 Speaker 1: and that that might even be easier to do in 123 00:06:47,476 --> 00:06:51,756 Speaker 1: relatively rural liberal arts colleges than it is to do 124 00:06:51,796 --> 00:06:55,716 Speaker 1: a new, major urban university. That's basically the premise, right 125 00:06:55,876 --> 00:06:57,996 Speaker 1: is like, because people we're talking about it right now 126 00:06:57,996 --> 00:07:01,436 Speaker 1: in the laboratories, and I'd sort of originally reached out 127 00:07:01,436 --> 00:07:05,756 Speaker 1: to laboratories that I know have residences on campus, saying, hey, 128 00:07:05,796 --> 00:07:07,956 Speaker 1: what's the chance we could commedy er your lab and 129 00:07:07,996 --> 00:07:10,596 Speaker 1: just create an institute there where you have a closed 130 00:07:10,596 --> 00:07:12,676 Speaker 1: circuit and you just make sure the infection doesn't get in. 131 00:07:12,956 --> 00:07:16,996 Speaker 1: A few investment banks did this with their traders. They thought, well, 132 00:07:16,996 --> 00:07:19,996 Speaker 1: our trading operation is absolutely crucial, and they took over hotels, 133 00:07:20,316 --> 00:07:23,036 Speaker 1: fancy hotels, but they took over the whole hotel and 134 00:07:23,116 --> 00:07:25,076 Speaker 1: isolated everybody and said here's where the team is going 135 00:07:25,076 --> 00:07:26,596 Speaker 1: to sit. And of course they got a lot of 136 00:07:26,596 --> 00:07:29,836 Speaker 1: bad publicity for that on the theory that somehow they 137 00:07:29,836 --> 00:07:32,396 Speaker 1: were putting capital first. And maybe that was true, But 138 00:07:32,716 --> 00:07:34,356 Speaker 1: what you're saying is that's actually not at all an 139 00:07:34,396 --> 00:07:36,556 Speaker 1: irrational way to think about it. If those folks never leave, 140 00:07:36,876 --> 00:07:38,996 Speaker 1: they become an isolated community and they can actually be 141 00:07:39,076 --> 00:07:41,276 Speaker 1: there and be relatively safe provided that the virus isn't 142 00:07:41,316 --> 00:07:44,916 Speaker 1: coming in. Yeah, and for me, from the standpoint of 143 00:07:45,596 --> 00:07:47,476 Speaker 1: for like a research scientist, I thought about this, and 144 00:07:47,476 --> 00:07:49,116 Speaker 1: there is bad press that you get from it, of like, 145 00:07:49,116 --> 00:07:51,116 Speaker 1: oh wait, the researchers have all hold themselves up and 146 00:07:51,116 --> 00:07:53,596 Speaker 1: protected themselves from others. It's more that I know that 147 00:07:53,876 --> 00:07:56,276 Speaker 1: my lab, for example, works on diagnostics or sequencing, and 148 00:07:56,316 --> 00:07:59,356 Speaker 1: I think that's important for the response. But if I'm 149 00:07:59,396 --> 00:08:01,036 Speaker 1: to do that work, the only way I could actually 150 00:08:01,036 --> 00:08:03,556 Speaker 1: do it in isolation putting myself in some sort of 151 00:08:03,556 --> 00:08:06,956 Speaker 1: like a protected shield, is to actually have a blogger 152 00:08:06,996 --> 00:08:09,476 Speaker 1: who every day says, this is what's going on. Let's 153 00:08:09,516 --> 00:08:12,156 Speaker 1: open this up to the entire world and say, let's 154 00:08:12,156 --> 00:08:14,116 Speaker 1: move as fast as we can. Let's bring everybody on 155 00:08:14,156 --> 00:08:16,236 Speaker 1: board of how fast we're trying to work. Can I 156 00:08:16,276 --> 00:08:19,396 Speaker 1: ask you to put on your virus tracker hat and 157 00:08:19,756 --> 00:08:22,876 Speaker 1: think about the virus as a strategic opponent, as you 158 00:08:22,916 --> 00:08:25,756 Speaker 1: often do. And I want to ask you about the 159 00:08:25,836 --> 00:08:30,276 Speaker 1: way that this particular coronavirus is spreading, first domestically in 160 00:08:30,276 --> 00:08:34,116 Speaker 1: the United States and then more broadly globally. Why are 161 00:08:34,156 --> 00:08:38,276 Speaker 1: we seeing such outsized outbreaks in certain locations New York 162 00:08:38,316 --> 00:08:43,476 Speaker 1: and maybe increasingly Boston, New Orleans, and less severe attacks 163 00:08:43,556 --> 00:08:45,716 Speaker 1: in places where it looked initially like things might be 164 00:08:45,796 --> 00:08:48,756 Speaker 1: just as bad. San Francisco is a good example. Why 165 00:08:48,756 --> 00:08:52,076 Speaker 1: don't we see a much broader degree of outbreaking? Is 166 00:08:52,076 --> 00:08:54,196 Speaker 1: that just a response to these social distancing measures? And 167 00:08:54,556 --> 00:08:56,476 Speaker 1: if so, why didn't it work as fast or as 168 00:08:56,476 --> 00:08:59,716 Speaker 1: effectively in places like Boston and New York. I think 169 00:08:59,796 --> 00:09:03,116 Speaker 1: that there's so many different parameters you have to think 170 00:09:03,196 --> 00:09:05,196 Speaker 1: about it. I mean, in some of the places where 171 00:09:05,196 --> 00:09:07,236 Speaker 1: you don't we're out seeing cases, it might actually be 172 00:09:07,356 --> 00:09:09,596 Speaker 1: that we're just not testing for them and there is 173 00:09:09,716 --> 00:09:13,036 Speaker 1: enough asymptomatics that we don't really have a good perspective 174 00:09:13,076 --> 00:09:15,876 Speaker 1: of what's going on. That said, you know, places like 175 00:09:15,956 --> 00:09:18,396 Speaker 1: Boston and New York have a lot of cases because 176 00:09:18,836 --> 00:09:22,196 Speaker 1: they are really dense populations, and I'll know San Francisco's one. 177 00:09:22,236 --> 00:09:24,156 Speaker 1: But I'll give a counterpoint to that in a second, 178 00:09:24,236 --> 00:09:26,356 Speaker 1: Like big cities are places where you're going to see 179 00:09:26,356 --> 00:09:28,556 Speaker 1: a lot of cases happen, because you know, people don't 180 00:09:28,556 --> 00:09:30,436 Speaker 1: want to be siloed in a tin can kind of 181 00:09:30,436 --> 00:09:33,556 Speaker 1: a room. Also, I mean I think Boston. When I 182 00:09:33,596 --> 00:09:36,036 Speaker 1: think about Boston, I think the first cases were actually 183 00:09:36,076 --> 00:09:39,996 Speaker 1: isolated very very well. The tipping point was this Biogen conference, 184 00:09:40,676 --> 00:09:42,676 Speaker 1: and I mean, I think that was just one of 185 00:09:42,716 --> 00:09:44,596 Speaker 1: those things. Conferences are places you bring a lot of 186 00:09:44,636 --> 00:09:47,276 Speaker 1: people in from all around the world, have them all 187 00:09:47,356 --> 00:09:49,156 Speaker 1: like hang out together in all crazy ways, and then 188 00:09:49,196 --> 00:09:51,716 Speaker 1: send them out into the world. And so a lot 189 00:09:51,796 --> 00:09:53,556 Speaker 1: of the outbreaks that were very bad just had a 190 00:09:53,636 --> 00:09:57,956 Speaker 1: bad entry event. So the Biogen conference set off something 191 00:09:58,076 --> 00:10:00,516 Speaker 1: in big motion. If I hear you correctly, what you're 192 00:10:00,516 --> 00:10:03,596 Speaker 1: saying is that there can be a lot of contingent luck, 193 00:10:03,716 --> 00:10:07,276 Speaker 1: good or bad in these circumstances. One event where there 194 00:10:07,396 --> 00:10:09,316 Speaker 1: may have been a super spreader or where people we're 195 00:10:09,316 --> 00:10:11,636 Speaker 1: all clumped together and then went to the four corners 196 00:10:11,676 --> 00:10:14,916 Speaker 1: of the Earth can really have a tremendous impact. Yeah, 197 00:10:15,036 --> 00:10:16,436 Speaker 1: Like you know when we talk about the sort of 198 00:10:16,956 --> 00:10:19,436 Speaker 1: can we come bring back residential colleges, I'm less worried 199 00:10:19,436 --> 00:10:21,876 Speaker 1: about residential colleges all as long as you because you 200 00:10:21,916 --> 00:10:23,916 Speaker 1: have this like longer time frame to think about people 201 00:10:23,956 --> 00:10:26,116 Speaker 1: going in and out as I am about conferences. This 202 00:10:26,236 --> 00:10:29,516 Speaker 1: is an existential threat to conferences, right, because conferences do 203 00:10:29,596 --> 00:10:31,436 Speaker 1: the perfect thing. They bring a lot of people from 204 00:10:31,476 --> 00:10:33,916 Speaker 1: around the world together, they read something for a while, 205 00:10:33,996 --> 00:10:36,116 Speaker 1: shake it up, and then send it out everywhere, and 206 00:10:36,276 --> 00:10:38,556 Speaker 1: so people will really have to think about those in 207 00:10:38,636 --> 00:10:40,996 Speaker 1: a very different way. And there's a chance that conferences 208 00:10:41,036 --> 00:10:43,436 Speaker 1: will come back for some time. I would be very 209 00:10:43,476 --> 00:10:46,076 Speaker 1: sad if we lost residential colleges, not to mention out 210 00:10:46,116 --> 00:10:48,596 Speaker 1: of a job, but if we lost academic conferences for 211 00:10:48,716 --> 00:10:50,356 Speaker 1: some period of time, I for one, but I will 212 00:10:50,396 --> 00:10:54,876 Speaker 1: not be profoundly crushed. Let me ask you about basically 213 00:10:54,916 --> 00:10:58,516 Speaker 1: the developing world. One way of thinking about this is 214 00:10:58,596 --> 00:11:03,796 Speaker 1: that we're not hearing about massive outbreaks of this particular 215 00:11:03,836 --> 00:11:07,516 Speaker 1: coronavirus in the developing world because there's not testing, but 216 00:11:07,636 --> 00:11:09,916 Speaker 1: it's in fact happening on a major scale right now, 217 00:11:09,996 --> 00:11:12,556 Speaker 1: we don't know about it. The other possibility is that 218 00:11:12,716 --> 00:11:15,596 Speaker 1: for some set of reasons, that hasn't happened yet, not 219 00:11:15,676 --> 00:11:17,516 Speaker 1: that it may not happen, but that it hasn't happened. 220 00:11:18,076 --> 00:11:20,756 Speaker 1: Do you have an instinct between those two answers, and 221 00:11:21,076 --> 00:11:23,756 Speaker 1: if so, which one? I don't have an answer, and 222 00:11:23,756 --> 00:11:25,596 Speaker 1: I could probably say it's like some of it's all 223 00:11:25,636 --> 00:11:28,236 Speaker 1: of the above. There. You know the fact that there's 224 00:11:28,276 --> 00:11:30,996 Speaker 1: not testing, ubiquitous testing is a lot of why you 225 00:11:31,036 --> 00:11:33,876 Speaker 1: don't see things. And we know that, like my group 226 00:11:33,916 --> 00:11:37,276 Speaker 1: wrote a paper in twenty twelve. It was a perspective 227 00:11:37,276 --> 00:11:40,796 Speaker 1: in Science magazine that said emerging disease or diagnosis question mark, 228 00:11:41,076 --> 00:11:43,476 Speaker 1: and it posed the question, are these diseases we think 229 00:11:43,516 --> 00:11:46,236 Speaker 1: are emerging actually emerging? Are we just emergingly? Do we 230 00:11:46,276 --> 00:11:48,596 Speaker 1: have emergent technologies to detect them? And have these things 231 00:11:48,676 --> 00:11:51,756 Speaker 1: been circulating for a long time? Because most viruses can 232 00:11:51,876 --> 00:11:54,756 Speaker 1: be asymptomatic or mildly symptomatic and just fly under the radar, 233 00:11:55,556 --> 00:11:59,276 Speaker 1: And so there COVID and other viruses could be breeding 234 00:11:59,316 --> 00:12:01,636 Speaker 1: and coming in even to patients at the hospital not 235 00:12:01,716 --> 00:12:04,596 Speaker 1: being detected because they didn't make enough of a statement 236 00:12:04,756 --> 00:12:06,796 Speaker 1: for us to be aware of them. So it's hard 237 00:12:06,836 --> 00:12:09,436 Speaker 1: to say. Are my collaborative, Christian Happy and his team 238 00:12:09,476 --> 00:12:13,116 Speaker 1: in Nigeria sequence the first case of COVID in Africa 239 00:12:13,236 --> 00:12:15,836 Speaker 1: and is part of a large group of people working 240 00:12:15,876 --> 00:12:18,716 Speaker 1: with the Nigerian CDC and others surveying what's going on 241 00:12:18,796 --> 00:12:20,676 Speaker 1: in Nigerian and their cases and their cases all throughout 242 00:12:20,716 --> 00:12:23,836 Speaker 1: the country. So it's definitely emergent. The one thing on 243 00:12:23,916 --> 00:12:25,556 Speaker 1: the other side I just want to make a point 244 00:12:25,596 --> 00:12:27,676 Speaker 1: of is should kind of come back to your first 245 00:12:27,796 --> 00:12:30,476 Speaker 1: question of like is it surprising to you that the 246 00:12:30,596 --> 00:12:33,356 Speaker 1: United States now is seeing this and experiencing this working 247 00:12:33,436 --> 00:12:34,796 Speaker 1: in Africa, and I was like, no, I've been more 248 00:12:34,836 --> 00:12:37,636 Speaker 1: worried about the United States than anywhere for a long time, 249 00:12:37,676 --> 00:12:41,316 Speaker 1: because actually, those other countries have been thinking about infectious 250 00:12:41,316 --> 00:12:43,236 Speaker 1: diseases for a long time, and they have an entire 251 00:12:43,636 --> 00:12:46,116 Speaker 1: network to manage this to some degree, So we shouldn't. 252 00:12:46,316 --> 00:12:48,076 Speaker 1: I'm not saying that there's not a lot that needs 253 00:12:48,116 --> 00:12:50,036 Speaker 1: to happen in those healthcare systems, but there's a lot 254 00:12:50,076 --> 00:12:52,476 Speaker 1: that they do have that we should appreciate, Like the 255 00:12:52,596 --> 00:12:55,476 Speaker 1: idea of healthcare workers to go out into people's homes. 256 00:12:55,676 --> 00:12:58,516 Speaker 1: They know that it's a bad idea to bring COVID 257 00:12:58,636 --> 00:13:00,956 Speaker 1: or a bowl of patients to the hospital in order 258 00:13:00,996 --> 00:13:03,316 Speaker 1: to be detected for a virus the way we do. 259 00:13:03,476 --> 00:13:05,516 Speaker 1: Like the hospitals are probably the breeding ground for most 260 00:13:05,556 --> 00:13:07,396 Speaker 1: of the infections. Somebody came in just to check in 261 00:13:07,476 --> 00:13:10,276 Speaker 1: on one thing and they left with COVID. The massive 262 00:13:11,156 --> 00:13:13,796 Speaker 1: health system that we have here is a problem for 263 00:13:13,876 --> 00:13:26,556 Speaker 1: infectious diseases. We'll be back in just a moment. Well, 264 00:13:26,596 --> 00:13:29,956 Speaker 1: the genomics work that you're doing now being most useful 265 00:13:30,476 --> 00:13:34,796 Speaker 1: for tracking, will be most useful for potential cure. What 266 00:13:34,916 --> 00:13:38,756 Speaker 1: are the different angles from which genomes can contribute going forward. 267 00:13:38,996 --> 00:13:42,076 Speaker 1: So there's genomics large and then there's genome sequencing, and 268 00:13:42,156 --> 00:13:46,396 Speaker 1: so for genome sequencing there are multiple great values. I 269 00:13:46,916 --> 00:13:49,556 Speaker 1: wrote an article early in the outbreak to say, actually, 270 00:13:49,596 --> 00:13:52,516 Speaker 1: we are in a good place because the folks in 271 00:13:52,556 --> 00:13:55,756 Speaker 1: Wuhan had identified the virus very early and had sequenced 272 00:13:55,756 --> 00:13:57,756 Speaker 1: the virus and made that data available to the community, 273 00:13:58,236 --> 00:14:01,196 Speaker 1: and the genome sequencing told us that the virus hadn't 274 00:14:01,236 --> 00:14:02,796 Speaker 1: been around very long, because it can give you a 275 00:14:02,876 --> 00:14:05,916 Speaker 1: sense of date how long a virus has been the population, 276 00:14:05,996 --> 00:14:08,316 Speaker 1: and the evidence from the genome sequencing tools it had 277 00:14:08,356 --> 00:14:11,396 Speaker 1: just recently urged into the human population. And then we 278 00:14:11,436 --> 00:14:14,636 Speaker 1: already had this genome sequencing data for it, which made 279 00:14:14,636 --> 00:14:17,076 Speaker 1: it possible so that labs all over the world, including 280 00:14:17,156 --> 00:14:19,636 Speaker 1: my own and many you know, my colleagues, had working 281 00:14:19,676 --> 00:14:22,196 Speaker 1: diagnostics within days of the paper being published, Like that's 282 00:14:22,276 --> 00:14:25,076 Speaker 1: sort of that's what we do. And as the virus changes, 283 00:14:25,116 --> 00:14:26,956 Speaker 1: it can improve, Like we're already seeing that a lot 284 00:14:26,996 --> 00:14:29,596 Speaker 1: of the diagnostics out there, the virus has already mutated 285 00:14:29,916 --> 00:14:34,956 Speaker 1: against them, so that the diagnostics actually tag the genome sequence, 286 00:14:34,996 --> 00:14:38,116 Speaker 1: and the diagnostics have to be kept updated. So that's 287 00:14:38,236 --> 00:14:40,996 Speaker 1: an important piece of that is that we didn't use 288 00:14:40,996 --> 00:14:43,836 Speaker 1: that information the right way because then we waited for 289 00:14:43,916 --> 00:14:45,356 Speaker 1: a long time and there are a lot of regulatory 290 00:14:45,436 --> 00:14:49,636 Speaker 1: barriers to having hospitals around the country have those diagnostics available. 291 00:14:49,916 --> 00:14:53,876 Speaker 1: But also it is really important for understanding transmission events, 292 00:14:54,396 --> 00:14:58,516 Speaker 1: understanding how many cases there are, where they're coming from, 293 00:14:58,556 --> 00:15:01,116 Speaker 1: where the introductions. It gives you a lot of information, 294 00:15:01,196 --> 00:15:03,476 Speaker 1: particularly as we try to wind down. It'll give us 295 00:15:03,516 --> 00:15:05,636 Speaker 1: a sense about how many cases are likely circulating in 296 00:15:05,676 --> 00:15:08,916 Speaker 1: this population. It can really inform epidemiology and how we 297 00:15:09,276 --> 00:15:12,356 Speaker 1: how we think about our response. As you watch the 298 00:15:12,476 --> 00:15:17,556 Speaker 1: speed with which this particular virus is evolving, does that 299 00:15:17,676 --> 00:15:20,436 Speaker 1: give you an intuition Obviously you can't make a firm prediction, 300 00:15:20,476 --> 00:15:23,636 Speaker 1: but does it give you an intuition about the question 301 00:15:23,716 --> 00:15:26,636 Speaker 1: of vaccine. So one of the great things that we 302 00:15:26,756 --> 00:15:30,036 Speaker 1: have going for us here many people believe that SARS 303 00:15:30,116 --> 00:15:33,036 Speaker 1: or stars like virus is going to be a next 304 00:15:33,076 --> 00:15:35,676 Speaker 1: big pandemic, and for that reason, a lot of the 305 00:15:35,756 --> 00:15:38,076 Speaker 1: vaccines that have had an early start and we really 306 00:15:38,116 --> 00:15:40,196 Speaker 1: got off to the races were people who had been 307 00:15:40,236 --> 00:15:42,676 Speaker 1: working for some time on Stars, and this virus is 308 00:15:42,716 --> 00:15:44,956 Speaker 1: not that different from SARS, and so we're already in 309 00:15:44,996 --> 00:15:49,836 Speaker 1: a really good position having spent time developing vaccines, diagnostics, 310 00:15:49,876 --> 00:15:52,836 Speaker 1: other things for Stars. And as far as you know, 311 00:15:52,956 --> 00:15:55,996 Speaker 1: will we have a long term vaccine that will capture 312 00:15:56,036 --> 00:16:00,196 Speaker 1: all of COVID forever? Probably not, But will we have 313 00:16:00,316 --> 00:16:02,396 Speaker 1: something that will make a big difference in people's lives 314 00:16:02,516 --> 00:16:06,236 Speaker 1: right now and kind of stop the major event? I 315 00:16:06,356 --> 00:16:08,836 Speaker 1: think so. The fact that there weren't really good animal 316 00:16:08,916 --> 00:16:15,036 Speaker 1: models for COVID early on was a bonus because companies 317 00:16:15,076 --> 00:16:19,116 Speaker 1: like Maderna, who are being vaccines got to skip animal trials, 318 00:16:19,156 --> 00:16:21,716 Speaker 1: which is a really can slow the vaccine development process. 319 00:16:21,716 --> 00:16:23,516 Speaker 1: So they went straight to human clinical trials, which is 320 00:16:23,596 --> 00:16:26,796 Speaker 1: unheard of. Hopefully the human clinical trials go well and 321 00:16:26,796 --> 00:16:28,996 Speaker 1: they're safe, because that is a risk jumping straight to 322 00:16:29,436 --> 00:16:32,596 Speaker 1: human trials. But whatever that outcome, it did move things 323 00:16:32,636 --> 00:16:35,436 Speaker 1: forward much much more quickly, and so we may say 324 00:16:35,516 --> 00:16:37,596 Speaker 1: vaccine that can help to really stem the tide soon, 325 00:16:37,956 --> 00:16:40,196 Speaker 1: and when it will be manufactured and when it will 326 00:16:40,236 --> 00:16:42,516 Speaker 1: get to all the corners of the earth, that's a 327 00:16:42,556 --> 00:16:44,596 Speaker 1: whole different question, and that's why people are talking about 328 00:16:44,596 --> 00:16:46,436 Speaker 1: the fact that we may be social distancing to twenty 329 00:16:46,476 --> 00:16:48,196 Speaker 1: twenty two, because that might be how long it takes 330 00:16:48,236 --> 00:16:50,596 Speaker 1: to really get you know, where we need to be. 331 00:16:50,676 --> 00:16:52,436 Speaker 1: And there's obviously a lot of debate on that too, 332 00:16:52,516 --> 00:16:56,276 Speaker 1: but it's something as a possibility perdue in the best 333 00:16:56,316 --> 00:16:59,756 Speaker 1: case scenario where we do get to a functional vaccine 334 00:16:59,796 --> 00:17:04,716 Speaker 1: relatively quickly, what are the barriers to getting it out 335 00:17:04,836 --> 00:17:07,476 Speaker 1: to the world that push us into this possible twenty 336 00:17:07,556 --> 00:17:11,196 Speaker 1: twenty two scenario. Are such vaccines just very difficult to 337 00:17:11,356 --> 00:17:15,156 Speaker 1: manufacture at scale? Is that the challenge? Yeah? I think 338 00:17:15,236 --> 00:17:18,716 Speaker 1: that in general, it does take a while to manufacture vaccines. 339 00:17:18,916 --> 00:17:21,196 Speaker 1: There's a lot of work that's been done toward improving 340 00:17:21,556 --> 00:17:24,996 Speaker 1: a vaccine manufacturer. But take, for example, the flu vaccine. 341 00:17:25,316 --> 00:17:28,036 Speaker 1: It takes six months to a year to really create 342 00:17:28,396 --> 00:17:31,876 Speaker 1: enough of that vaccine to make available to everybody. Couldn't 343 00:17:31,876 --> 00:17:33,956 Speaker 1: that be scaled up though very substantially. I mean the 344 00:17:33,996 --> 00:17:36,396 Speaker 1: reason I say this is I had Larry Summers on 345 00:17:36,476 --> 00:17:38,476 Speaker 1: the show the other day and he was saying, it's 346 00:17:38,516 --> 00:17:41,996 Speaker 1: costing the United States economy eighty billion dollars a week 347 00:17:42,556 --> 00:17:45,276 Speaker 1: every week that we're shut down. He said, so anything 348 00:17:45,356 --> 00:17:48,156 Speaker 1: that you spent that is not more than eighty billion 349 00:17:48,196 --> 00:17:50,796 Speaker 1: dollars a week is money saving. We're definitely not talking 350 00:17:50,836 --> 00:17:53,996 Speaker 1: about that scale. So I mean, are there actual bottlenecks 351 00:17:54,036 --> 00:17:56,716 Speaker 1: here that could be overcome? Well, I mean, that's again 352 00:17:56,836 --> 00:17:58,596 Speaker 1: coming back to the whole point of the other part 353 00:17:58,596 --> 00:18:00,756 Speaker 1: of our conversation, which is how do we get people 354 00:18:00,836 --> 00:18:02,676 Speaker 1: back to work if they're social distancing at work? And 355 00:18:02,756 --> 00:18:04,516 Speaker 1: we I mean, I only have about a third of 356 00:18:04,596 --> 00:18:06,516 Speaker 1: my team able to work right now. That's why I 357 00:18:06,596 --> 00:18:08,396 Speaker 1: started being obsessed with the idea of how do I 358 00:18:08,596 --> 00:18:11,556 Speaker 1: comedy or somebody's lab with residential housing and how to 359 00:18:11,676 --> 00:18:14,476 Speaker 1: put people in because that is the interesting dance that 360 00:18:14,556 --> 00:18:16,796 Speaker 1: we have, which is that we are trying to do 361 00:18:16,876 --> 00:18:18,836 Speaker 1: it in the midst of a lockdown, So how do 362 00:18:18,996 --> 00:18:21,876 Speaker 1: we get essential personnel back and running? So no, we 363 00:18:21,956 --> 00:18:24,716 Speaker 1: actually are going to be going slower right now based 364 00:18:24,796 --> 00:18:26,876 Speaker 1: on the practices we have of getting things back up 365 00:18:26,916 --> 00:18:29,196 Speaker 1: to speed. And so you know, I was actually talking 366 00:18:29,236 --> 00:18:30,796 Speaker 1: about because diagnostics are one of the first things we 367 00:18:30,876 --> 00:18:32,236 Speaker 1: need to get to be able to get surveillance to 368 00:18:32,276 --> 00:18:34,276 Speaker 1: move things forward. And so I was like, the diagnostics 369 00:18:34,356 --> 00:18:36,796 Speaker 1: groups that are building and manufacturing the new diagnostics that 370 00:18:36,836 --> 00:18:40,116 Speaker 1: are FDA approved. One may argue that they should be 371 00:18:40,236 --> 00:18:42,356 Speaker 1: test doing their own testing and using something testing on 372 00:18:42,396 --> 00:18:44,916 Speaker 1: their own people on a regular basis, because if they 373 00:18:45,036 --> 00:18:47,436 Speaker 1: shut down then they can't get more diagnostics to the world. 374 00:18:47,516 --> 00:18:50,156 Speaker 1: So we are and I agree with you that we 375 00:18:50,236 --> 00:18:52,156 Speaker 1: should be trying to move it forward. There are some 376 00:18:52,996 --> 00:18:55,756 Speaker 1: just infrastructurally, we can't go much much faster unless we 377 00:18:55,836 --> 00:18:59,276 Speaker 1: build new plants altogether, and so I think we could 378 00:18:59,316 --> 00:19:02,116 Speaker 1: all be thinking about that right how to create plants 379 00:19:02,156 --> 00:19:04,596 Speaker 1: that themselves have an ecosystem where they can keep running. 380 00:19:05,996 --> 00:19:09,476 Speaker 1: Let me ask you about the future, the post COVID future. 381 00:19:10,036 --> 00:19:12,676 Speaker 1: We may never be a fully post COVID, but you've 382 00:19:12,716 --> 00:19:14,756 Speaker 1: done a huge amount of work on early detection. That's 383 00:19:14,796 --> 00:19:18,316 Speaker 1: been one of your central goals. What kinds of plans 384 00:19:18,356 --> 00:19:21,156 Speaker 1: and programs do you have in the works, because my 385 00:19:21,276 --> 00:19:24,716 Speaker 1: guess as those will now be extremely popular and likely 386 00:19:24,756 --> 00:19:28,596 Speaker 1: to be very well funded. Yeah, so my lab has 387 00:19:28,596 --> 00:19:31,196 Speaker 1: been really passionate about early detection for a very very 388 00:19:31,276 --> 00:19:34,636 Speaker 1: long time. Began with our work on lass of fever, 389 00:19:34,796 --> 00:19:39,116 Speaker 1: but then really was really came to bear during the 390 00:19:39,396 --> 00:19:43,596 Speaker 1: able epidemic, seeing that this outbreak likely happened in some 391 00:19:44,076 --> 00:19:47,876 Speaker 1: remote village in Guinea, you know, with one boy, and 392 00:19:47,996 --> 00:19:50,156 Speaker 1: if it had been detected there, the outbreak might have 393 00:19:50,196 --> 00:19:53,076 Speaker 1: been averted. But instead it was four months later with 394 00:19:53,196 --> 00:19:55,916 Speaker 1: cases across the country where we became aware of ebola, 395 00:19:56,316 --> 00:19:57,796 Speaker 1: and the same thing here in the United States. I 396 00:19:57,876 --> 00:20:00,356 Speaker 1: mean many many cases and time passed before we were 397 00:20:00,396 --> 00:20:04,876 Speaker 1: aware of COVID, and with COVID we saw things faster. 398 00:20:05,036 --> 00:20:07,756 Speaker 1: But COVID moves so much faster that it really does 399 00:20:07,836 --> 00:20:11,836 Speaker 1: show you you need very rapid detection. And so we 400 00:20:12,036 --> 00:20:16,596 Speaker 1: have been envisioning with our partners in Africa something called Sentinel, 401 00:20:16,956 --> 00:20:19,116 Speaker 1: which is essentially this idea that each of us stand 402 00:20:19,196 --> 00:20:21,796 Speaker 1: guards over the other, and if we create a system 403 00:20:21,836 --> 00:20:24,476 Speaker 1: by which every person is cared for within the system, 404 00:20:24,876 --> 00:20:27,076 Speaker 1: that will allow us to stand guard on each other 405 00:20:27,516 --> 00:20:30,756 Speaker 1: and identify what we have share that information with everybody. 406 00:20:30,796 --> 00:20:33,196 Speaker 1: And so we help and partner with a lot of 407 00:20:33,236 --> 00:20:38,356 Speaker 1: different groups developing very creative diagnostic tools and then sequencing 408 00:20:38,396 --> 00:20:40,396 Speaker 1: to be able to identify new things that are just 409 00:20:40,556 --> 00:20:44,156 Speaker 1: completely unheard of and just kind of keep characterizing what's 410 00:20:44,196 --> 00:20:47,636 Speaker 1: out there. And then a dashboard that includes these mobile 411 00:20:47,636 --> 00:20:50,996 Speaker 1: applications that allow you to report to healthcare when you're 412 00:20:50,996 --> 00:20:53,476 Speaker 1: sick and to get diagnostic information and share that with 413 00:20:53,556 --> 00:20:58,796 Speaker 1: your community through the healthcare system. Also dashboards that allow 414 00:20:58,876 --> 00:21:01,476 Speaker 1: everyone to immediately see where clusters of cases are emerging 415 00:21:01,516 --> 00:21:05,796 Speaker 1: and beyond. And so that is the world we see that. 416 00:21:05,916 --> 00:21:08,836 Speaker 1: The idea behind all of it is empowering every actor 417 00:21:08,916 --> 00:21:10,916 Speaker 1: in the system, empowering every individual to be able to 418 00:21:10,956 --> 00:21:13,436 Speaker 1: get to the care they need immediately, empowering the healthcare 419 00:21:13,476 --> 00:21:15,716 Speaker 1: workers to engage with them and to get the best 420 00:21:15,756 --> 00:21:18,676 Speaker 1: information in real time. And that will be the world, 421 00:21:18,756 --> 00:21:21,436 Speaker 1: you know, where everyone is engaged, everyone feels empowered, everyone 422 00:21:21,516 --> 00:21:23,796 Speaker 1: has the information that they need, and we can have that. 423 00:21:23,996 --> 00:21:26,796 Speaker 1: I mean, the technology is there, the funding has not 424 00:21:26,916 --> 00:21:30,156 Speaker 1: been historically and that may change, and I think the 425 00:21:30,236 --> 00:21:32,796 Speaker 1: will seems to be clear, and definitely the case has 426 00:21:32,836 --> 00:21:35,156 Speaker 1: been made. COVID has made that case for us of 427 00:21:35,316 --> 00:21:38,196 Speaker 1: the value of having something like this perde Before I 428 00:21:38,276 --> 00:21:41,036 Speaker 1: let you go, what am I not asking you that's 429 00:21:41,116 --> 00:21:44,916 Speaker 1: really salient to you? I mean, you already were my 430 00:21:45,036 --> 00:21:47,796 Speaker 1: go to pandemic person before any of this happened, and 431 00:21:47,876 --> 00:21:51,036 Speaker 1: I'm thrilled to have you here for the moment. What 432 00:21:51,196 --> 00:21:53,476 Speaker 1: do you want to tell the listeners about the situation 433 00:21:53,516 --> 00:21:56,196 Speaker 1: that we're in and about where we might go. Gosh, 434 00:21:56,316 --> 00:21:57,716 Speaker 1: a lot of things that I want to hear from 435 00:21:57,756 --> 00:21:59,116 Speaker 1: a lot of your listeners. I mean, I think that 436 00:21:59,476 --> 00:22:01,796 Speaker 1: to me, actually, like the listening part is so important, 437 00:22:01,796 --> 00:22:04,956 Speaker 1: and I realize like most of my ideas and thoughts 438 00:22:04,956 --> 00:22:06,516 Speaker 1: that I have is just by trying to be a 439 00:22:06,596 --> 00:22:10,116 Speaker 1: good listener and learning from people. And this is a 440 00:22:10,236 --> 00:22:12,796 Speaker 1: moment in which it's like it's one of the most 441 00:22:13,036 --> 00:22:16,396 Speaker 1: human moments because there's this existential threat that binds us 442 00:22:16,396 --> 00:22:18,876 Speaker 1: all that requires every person to think of each other 443 00:22:20,036 --> 00:22:21,796 Speaker 1: to do it right. You know, you can, you can 444 00:22:21,796 --> 00:22:23,276 Speaker 1: do the thing where we distance each other and hate 445 00:22:23,316 --> 00:22:24,996 Speaker 1: on each other, but actually to really do it, it's 446 00:22:24,996 --> 00:22:29,156 Speaker 1: a cooperative game against a common enemy. And in my mind, 447 00:22:29,236 --> 00:22:31,116 Speaker 1: I think the thing I just really want is about 448 00:22:31,196 --> 00:22:33,996 Speaker 1: thinking about our communities, thinking about how to engage everybody 449 00:22:34,036 --> 00:22:36,836 Speaker 1: and to work together. You'll actually do your best work 450 00:22:36,876 --> 00:22:40,076 Speaker 1: and probably get the most reward by being the most generous. 451 00:22:40,996 --> 00:22:43,636 Speaker 1: I made my probably my best professional success was by 452 00:22:43,676 --> 00:22:46,756 Speaker 1: sharing data which I did not to get credit, and 453 00:22:46,836 --> 00:22:48,636 Speaker 1: that got me credit. And so this is a moment 454 00:22:49,036 --> 00:22:51,236 Speaker 1: to not think about getting credit, and that's how we 455 00:22:51,316 --> 00:22:53,316 Speaker 1: will all get the most benefit and the most credit. 456 00:22:53,676 --> 00:22:55,796 Speaker 1: And I just hope that for the world that we 457 00:22:55,996 --> 00:22:59,876 Speaker 1: just hone our skills by thinking about each person, each 458 00:22:59,916 --> 00:23:01,636 Speaker 1: of our neighbors, and each of our fellow citizens on 459 00:23:01,676 --> 00:23:05,236 Speaker 1: the earth. Thank you, Bertie. That's a truly inspiring vision 460 00:23:05,236 --> 00:23:06,756 Speaker 1: of how we ought to go. And it's a ray 461 00:23:06,796 --> 00:23:09,876 Speaker 1: of sunshine and what is otherwise a very time thank you. 462 00:23:10,156 --> 00:23:12,516 Speaker 1: Thanks though I was such a pleasure to do this, 463 00:23:12,676 --> 00:23:14,876 Speaker 1: wish wasn't under these circumstances right now. Well, we'll see 464 00:23:14,876 --> 00:23:17,836 Speaker 1: each other in persons soon enough, before twenty twenty two, hope. 465 00:23:17,876 --> 00:23:22,236 Speaker 1: So all right, take care, take care bye. As always, 466 00:23:22,476 --> 00:23:26,596 Speaker 1: Partise is thinking outside the box about viruses and how 467 00:23:26,636 --> 00:23:29,876 Speaker 1: to fight them. Unlike just about everybody else who thinks 468 00:23:29,916 --> 00:23:34,276 Speaker 1: dorms are the danger, Pardise raises the possibility that university 469 00:23:34,356 --> 00:23:38,516 Speaker 1: campuses might be ideal circumstances for us to begin the 470 00:23:38,636 --> 00:23:43,276 Speaker 1: fight against coronavirus by using contact tracing and a high 471 00:23:43,396 --> 00:23:48,636 Speaker 1: degree of voluntary surveillance. Academic conferences, she says, are the enemy, 472 00:23:49,036 --> 00:23:50,996 Speaker 1: not for the reason that they make you crazy, but 473 00:23:51,156 --> 00:23:53,716 Speaker 1: for the reason that they spread disease. Those, she says, 474 00:23:53,876 --> 00:23:56,436 Speaker 1: may not be back for the foreseeable future. And I 475 00:23:56,636 --> 00:23:59,716 Speaker 1: for one am not going to mourn that, But perhaps 476 00:23:59,836 --> 00:24:04,036 Speaker 1: most significantly, partis wants us to remember that the way 477 00:24:04,156 --> 00:24:08,436 Speaker 1: we succeed in fighting COVID is by viewing ourselves as 478 00:24:08,476 --> 00:24:13,076 Speaker 1: engaged in a cooperative endeavor where we all together have 479 00:24:13,236 --> 00:24:17,076 Speaker 1: to defeat a common enemy. That's a message that's all 480 00:24:17,236 --> 00:24:20,836 Speaker 1: too easy to miss in our current political moment, and 481 00:24:21,036 --> 00:24:25,076 Speaker 1: yet it's vastly significant, and for me, that's the most 482 00:24:25,116 --> 00:24:28,276 Speaker 1: powerful takeaway of what parties had to share with us today. 483 00:24:30,636 --> 00:24:33,556 Speaker 1: Deep Background is brought to you by Pushkin Industries. Our 484 00:24:33,636 --> 00:24:37,596 Speaker 1: producer is Lydia gene Conn with research help from Zoie Dwynn. 485 00:24:38,076 --> 00:24:41,716 Speaker 1: Mastering is by Jason Gambrel and Martin Gonzalez. Our showrunner 486 00:24:41,756 --> 00:24:44,996 Speaker 1: is Sophie mckibbon. Our theme music is composed by Luis Guerra. 487 00:24:45,556 --> 00:24:49,156 Speaker 1: Special thanks to the Pushkin Brass, Malcolm Gladwell, Jacob Weisberg, 488 00:24:49,236 --> 00:24:52,436 Speaker 1: and Mia Lobel. I'm Noah Feld. I also write a 489 00:24:52,476 --> 00:24:55,076 Speaker 1: regular column from Bloomberg Opinion, which you can find at 490 00:24:55,116 --> 00:24:59,436 Speaker 1: Bloomberg dot com slash feld. To discover Bloomberg's original slate 491 00:24:59,476 --> 00:25:03,716 Speaker 1: of podcasts, go to Bloomberg dot com slash Podcasts. You 492 00:25:03,796 --> 00:25:06,796 Speaker 1: can follow me on Twitter at Noah r. Feld. This 493 00:25:07,396 --> 00:25:08,236 Speaker 1: is deep backward